# Computations
import pandas as pd
import numpy as np

# sklearn
from sklearn.impute import SimpleImputer

# Tools
import os
import datetime
import itertools

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## matplotlib
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
from matplotlib.font_manager import FontProperties
plt.rcParams['figure.figsize'] = 14, 8
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## seaborn
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper', rc={'font.size':12,'axes.titlesize':14,'axes.labelsize':12})
plt.style.use('seaborn-whitegrid')

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Movie Recommendation System

In this article, a recommender system is developed that suggests similar movies based on the MovieLens datasets.

Datasets

def Header(Text, L = 100, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
    
def Line(L=100, C = 'Blue'):
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)

def Search_List(Key, List): return [s for s in List if Key in s]

PATH = 'movielens'
# The full dataset
## col names from the readme file
Cols = ['user id','item id','rating','timestamp']
Cols = [x.title().replace('Id','ID')  for x in Cols]
data_df = pd.read_csv(os.path.join(PATH, 'u.data'), sep='\t', names= Cols)
data_df = data_df.rename(columns = {'Item ID': 'Movie ID'})
Header('Full dataset')
display(data_df)

# Information about the movies
## col names from the readme file
Cols = ['movie id','movie title','release date','video release date','IMDb URL','unknown','Action','Adventure',
        'Animation',"""Children's""",'Comedy','Crime','Documentary','Drama','Fantasy','Film-Noir','Horror','Musical',
        'Mystery','Romance','Sci-Fi','Thriller','War','Western']
Cols = [x.title().replace('Id','ID')  for x in Cols]
item_df = pd.read_csv(os.path.join(PATH, 'u.item'), sep='|', names= Cols, encoding = 'iso-8859-1')
item_df.drop(columns = ['Video Release Date', 'Imdb Url'], inplace = True)
Header('Information about the movies')
display(item_df)

# Demographic information about the users
## col names from the readme file
Cols = ['user id','age','gender','occupation','zip code']
Cols = [x.title().replace('Id','ID')  for x in Cols]
user_df = pd.read_csv(os.path.join(PATH, 'u.user'), sep='|', names= Cols)
Header('Demographic information about the users')
user_df.replace({'M':'Male', 'F':'Female'}, inplace = True)
user_df['Occupation'] = user_df['Occupation'].apply(lambda x: x.title())
display(user_df)
Line()
del Cols

Full dataset =======================================================================================

	User ID	Movie ID	Rating	Timestamp
0	196	242	3	881250949
1	186	302	3	891717742
2	22	377	1	878887116
3	244	51	2	880606923
4	166	346	1	886397596
...	...	...	...	...
99995	880	476	3	880175444
99996	716	204	5	879795543
99997	276	1090	1	874795795
99998	13	225	2	882399156
99999	12	203	3	879959583

100000 rows × 4 columns

Information about the movies =======================================================================

	Movie ID	Movie Title	Release Date	Unknown	Action	Adventure	Animation	Children'S	Comedy	Crime	...	Fantasy	Film-Noir	Horror	Musical	Mystery	Romance	Sci-Fi	Thriller	War	Western
0	1	Toy Story (1995)	01-Jan-1995	0	0	0	1	1	1	0	...	0	0	0	0	0	0	0	0	0	0
1	2	GoldenEye (1995)	01-Jan-1995	0	1	1	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
2	3	Four Rooms (1995)	01-Jan-1995	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
3	4	Get Shorty (1995)	01-Jan-1995	0	1	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0
4	5	Copycat (1995)	01-Jan-1995	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1677	1678	Mat' i syn (1997)	06-Feb-1998	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1678	1679	B. Monkey (1998)	06-Feb-1998	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	1	0	0
1679	1680	Sliding Doors (1998)	01-Jan-1998	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	0	0	0
1680	1681	You So Crazy (1994)	01-Jan-1994	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0
1681	1682	Scream of Stone (Schrei aus Stein) (1991)	08-Mar-1996	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

1682 rows × 22 columns

Demographic information about the users ============================================================

	User ID	Age	Gender	Occupation	Zip Code
0	1	24	Male	Technician	85711
1	2	53	Female	Other	94043
2	3	23	Male	Writer	32067
3	4	24	Male	Technician	43537
4	5	33	Female	Other	15213
...	...	...	...	...	...
938	939	26	Female	Student	33319
939	940	32	Male	Administrator	02215
940	941	20	Male	Student	97229
941	942	48	Female	Librarian	78209
942	943	22	Male	Student	77841

943 rows × 5 columns

====================================================================================================

Preprocessing

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out

display(Data_info(item_df, Only_NaN = True))

	Data Type	Number of NaN Values	Percentage
Release Date	object	1	0.06

Imputing these missing values.

imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Temp = imp.fit_transform(item_df['Release Date'].values.reshape(-1,1))
item_df['Release Date'] = Temp
del Temp

Creating Movie info dataset (for analysis only)

# Release date to datetime
item_df['Release Date'] = pd.to_datetime(item_df['Release Date'])
# adding release year
item_df['Release Year'] = pd.to_numeric(item_df['Release Date'].dt.year, downcast = 'integer')
# Removing year from movie title
# item_df['Movie Title'] = item_df['Movie Title'].str.replace(r'[\(\)\d]+', '')
item_df['Movie Title'] = item_df['Movie Title'].str.replace(r'\(\d+\)', '').apply(lambda x: x.rstrip())
# Creating a new database for our analysis
Temp = item_df.iloc[:,3:]
Temp['Movie ID'] = item_df['Movie ID']
Temp.set_index('Movie ID',inplace=True)
Temp = Temp[Temp==1].stack().reset_index().drop(0,1).rename(columns = {'level_1':'Genre'})
Temp = Temp.groupby(['Movie ID'])['Genre'].apply(lambda x: ', '.join(x)).reset_index()
Movie_info = item_df.iloc[:,[0,1,2,-1]].merge(Temp, how = 'outer', on = 'Movie ID')
del Temp
item_df.to_csv('movielens/item_df.csv',index=False)

# full dataset
df = data_df.merge(user_df, how = 'outer', on = 'User ID')

Age Groups

We can create Age Categories using statcan.gc.ca.

def Age_Group(Data):
    if Data['Age'].min() < 14:
        bins = pd.IntervalIndex.from_tuples([(0, 14), (14, 24), (24, 64),(64, Data.Age.max())])
    else:
        bins = pd.IntervalIndex.from_tuples([(14, 24), (24, 64),(64, Data.Age.max())])
    Data['Age Group'] = pd.cut(Data['Age'], bins)
    Data['Age Category'] = Data['Age Group'].astype(str).replace({'(0, 14]': 'Children',
                                                                  '(14, 24]':'Youth',
                                                                  '(24, 64]':'Adults',
                                                                  '(64, ' + str(Data['Age'].max()) + ']':'Seniors'})
    return Data

df = Age_Group(df)
df.to_csv('movielens/full_df.csv',index=False)

Data Analysis

Creating a dataset that we use here for our analysis

df = df.merge(Movie_info, how = 'outer', on = 'Movie ID').sort_values(by=['User ID'])
# Full dataset that we use here for our analysis
display(df)

	User ID	Movie ID	Rating	Timestamp	Age	Gender	Occupation	Zip Code	Age Group	Age Category	Movie Title	Release Date	Release Year	Genre
30897	1	161	4	875072303	24	Male	Technician	85711	(14, 24]	Youth	Top Gun	1986-01-01	1986	Action, Romance
30527	1	144	4	875073180	24	Male	Technician	85711	(14, 24]	Youth	Die Hard	1988-01-01	1988	Action, Thriller
82356	1	41	2	876892818	24	Male	Technician	85711	(14, 24]	Youth	Billy Madison	1995-01-01	1995	Comedy
5247	1	13	5	875071805	24	Male	Technician	85711	(14, 24]	Youth	Mighty Aphrodite	1995-10-30	1995	Comedy
24073	1	208	5	878542960	24	Male	Technician	85711	(14, 24]	Youth	Young Frankenstein	1974-01-01	1974	Comedy, Horror
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
15325	943	568	3	888639042	22	Male	Student	77841	(14, 24]	Youth	Speed	1994-01-01	1994	Action, Romance, Thriller
42349	943	1047	2	875502146	22	Male	Student	77841	(14, 24]	Youth	Multiplicity	1996-07-12	1996	Comedy
43832	943	685	4	875502042	22	Male	Student	77841	(14, 24]	Youth	Executive Decision	1996-03-09	1996	Action, Thriller
93741	943	1330	3	888692465	22	Male	Student	77841	(14, 24]	Youth	An Unforgettable Summer	1994-01-01	1994	Drama
48091	943	401	1	888639867	22	Male	Student	77841	(14, 24]	Youth	Brady Bunch Movie, The	1995-01-01	1995	Comedy

100000 rows × 14 columns

Most Watched Movies by Gender

Temp = df.groupby(by = ['Movie Title','Gender'])['Movie Title'].agg({'count'}).rename(columns = {'count':'Count'}).reset_index()
Temp['Percentage'] = np.round(100* Temp['Count'] /Temp['Count'].sum(), 2)
Temp.sort_values(by=['Gender', 'Count'], inplace = True, ascending = False)

fig = make_subplots(rows=1, cols=1, vertical_spacing = 0.08)
fig1 = px.bar(Temp.loc[Temp['Movie Title'].isin(Temp['Movie Title'][:20].unique().tolist())],
              x= 'Movie Title', y= 'Percentage', orientation='v', color = 'Gender', text = 'Percentage',
              hover_data= Temp.columns, color_discrete_sequence = ['Violet', 'BlueViolet'])
for j in range(len(fig1['data'])):
    fig.add_trace(fig1['data'][j], row=1, col=1)

# Update
fig.update_layout(height= 600)
fig.update_traces(marker_line_color= 'Black', marker_line_width=1, opacity=1, row=1, col=1)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='v')
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition= 'outside')
fig.update_layout(title={'text': 'Most Watched Movie Titles by Gender',
                         'x':0.50, 'y': 0.9, 'xanchor': 'center', 'yanchor': 'top'})
fig.show()
del Temp

Ratings

Ratings = df.groupby(['Movie Title'])['Rating'].agg({'count','mean'})
Ratings.columns =  ['Average Rating', 'Number of Ratings']
Ratings.sort_values(by=['Number of Ratings'],ascending=False, inplace = True)
Ratings.reset_index(inplace = True)

Feat = 'Average Rating'
yLim = int(500)
fig = px.histogram(Ratings, x = Feat, nbins=10,  marginal= 'box', color_discrete_sequence= ['LimeGreen'],
                  hover_data=[Feat])
fig.update_layout(plot_bgcolor= 'white', height = 500)
fig.update_layout(title={'text': 'Average Rating Distribution', 'x':0.5, 'y':0.92, 'xanchor': 'center', 'yanchor': 'top'},
                  yaxis_title='Frequency')
fig.update_traces(marker_line_color= 'DarkGreen', marker_line_width=1.5, opacity=1)
fig.add_trace(go.Scatter(x= Ratings[Feat].median()* np.ones(yLim), y= np.arange(0,yLim),
                         name="Median", line=dict(color='RoyalBlue', width=2, dash='dot')))
fig.add_trace(go.Scatter(x= Ratings[Feat].mean()* np.ones(yLim), y= np.arange(0,yLim), name="Mean",
                         line=dict(color='Red', width=2, dash='dot')))
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', )
fig['layout']['yaxis'].update(range=[0, yLim])
fig.show()

Feat = 'Number of Ratings'
yLim = int(8e2)
fig = px.histogram(Ratings, x = Feat, nbins=50,  marginal= 'box', color_discrete_sequence= ['LightSkyblue'],
                  hover_data=[Feat])
fig.update_layout(plot_bgcolor= 'white', height = 500)
fig.update_layout(title={'text': 'Average Rating Distribution', 'x':0.5, 'y':0.92, 'xanchor': 'center', 'yanchor': 'top'},
                  yaxis_title='Frequency')
fig.update_traces(marker_line_color= 'Navy', marker_line_width=1.5, opacity=1)
fig.add_trace(go.Scatter(x= Ratings[Feat].median()* np.ones(yLim), y= np.arange(0,yLim),
                         name="Median", line=dict(color='RoyalBlue', width=2, dash='dot')))
fig.add_trace(go.Scatter(x= Ratings[Feat].mean()* np.ones(yLim), y= np.arange(0,yLim), name="Mean",
                         line=dict(color='Red', width=2, dash='dot')))
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, yLim])
fig.show()

Modeling

The next step is to create a matrix that has the user ids on one axis and the movie titles on another. Each cell will then consist of the rating of a movie by a particular user. For this purpose, we use pivot_table function (see this link for more details).

Movie_Matrix = df.pivot_table(index='User ID', columns='Movie Title', values='Rating')
Movie_Matrix.head()

Movie Title	'Til There Was You	1-900	101 Dalmatians	12 Angry Men	187	2 Days in the Valley	20,000 Leagues Under the Sea	2001: A Space Odyssey	3 Ninjas: High Noon At Mega Mountain	39 Steps, The	...	Yankee Zulu	Year of the Horse	You So Crazy	Young Frankenstein	Young Guns	Young Guns II	Young Poisoner's Handbook, The	Zeus and Roxanne	unknown	Á köldum klaka (Cold Fever)
User ID
1	NaN	NaN	2.0	5.0	NaN	NaN	3.0	4.0	NaN	NaN	...	NaN	NaN	NaN	5.0	3.0	NaN	NaN	NaN	4.0	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	NaN	NaN	2.0	NaN	NaN	NaN	NaN	4.0	NaN	NaN	...	NaN	NaN	NaN	4.0	NaN	NaN	NaN	NaN	4.0	NaN

5 rows × 1659 columns

Since users might have not watched all movies (only some movies), some of these values appear as NaN.

Checking out the top ten movies that have a rating greater than 4.0 and have the most rated numbers.

Top_Ratings = Ratings[Ratings['Average Rating'] > 4.0].sort_values('Number of Ratings',ascending=False)
Top_list = list(Top_Ratings['Movie Title']) 
Top = 10
print(Back.CYAN + "The Top %i by Average Rating:" % Top)
print(Style.RESET_ALL)
# 
display(Top_Ratings.head(Top).style.hide_index())

The Top 10 by Average Rating:

Movie Title	Average Rating	Number of Ratings
Star Wars	4.358491	583
Fargo	4.155512	508
Return of the Jedi	4.007890	507
Raiders of the Lost Ark	4.252381	420
Godfather, The	4.283293	413
Pulp Fiction	4.060914	394
Silence of the Lambs, The	4.289744	390
Empire Strikes Back, The	4.204360	367
Titanic	4.245714	350
Fugitive, The	4.044643	336

Developing a Recommender Function

In doing so, consider a movie title, for example,

Movie = Top_list[0]
print(Back.YELLOW +'"%s":' % Movie)

"Star Wars":

Now let's consider the user ratings for this movie

Temp = Movie_Matrix[Movie]

We can then use corrwith() method to get correlations between two pandas series:

similar_to_movie = Movie_Matrix.corrwith(Temp)
display(similar_to_movie.to_frame('Pairwise Correlation'))

	Pairwise Correlation
Movie Title
'Til There Was You	0.872872
1-900	-0.645497
101 Dalmatians	0.211132
12 Angry Men	0.184289
187	0.027398
...	...
Young Guns II	0.228615
Young Poisoner's Handbook, The	-0.007374
Zeus and Roxanne	0.818182
unknown	0.723123
Á köldum klaka (Cold Fever)	NaN

1659 rows × 1 columns

Cleaning the data by removing Nan values and using a DataFrame instead.

df_movie_corr = pd.DataFrame(similar_to_movie,columns=['Correlation']).reset_index(drop = False)
df_movie_corr.dropna(inplace=True)
print('Ten similar movies to "%s":' % Top_list[0])
df_movie_corr.sort_values('Correlation',ascending=False).head(10).style.hide_index()

Ten similar movies to "Star Wars":

Movie Title	Correlation
Man of the Year	1.000000
Commandments	1.000000
Cosi	1.000000
Stripes	1.000000
Star Wars	1.000000
Hollow Reed	1.000000
No Escape	1.000000
Outlaw, The	1.000000
Ed's Next Move	1.000000
Full Speed	1.000000

However, some of these movies only rated by a few users. In caste that we are only interested in movies that have a least 100 reviews. We have,

Ratings[['Movie Title','Number of Ratings']]

	Movie Title	Number of Ratings
0	Star Wars	583
1	Contact	509
2	Fargo	508
3	Return of the Jedi	507
4	Liar Liar	485
...	...	...
1654	Object of My Affection, The	1
1655	Office Killer	1
1656	Other Voices, Other Rooms	1
1657	Paris Was a Woman	1
1658	Á köldum klaka (Cold Fever)	1

1659 rows × 2 columns

df_movie_corr = pd.merge(df_movie_corr, Ratings[['Movie Title','Number of Ratings']] , on = 'Movie Title')
df_movie_corr.head()

	Movie Title	Correlation	Number of Ratings
0	'Til There Was You	0.872872	9
1	1-900	-0.645497	5
2	101 Dalmatians	0.211132	109
3	12 Angry Men	0.184289	125
4	187	0.027398	41

Now sort the values and notice how the titles make a lot more sense:

df_movie_corr[df_movie_corr['Number of Ratings']>100].sort_values('Correlation',ascending=False).head()

	Movie Title	Correlation	Number of Ratings
1188	Star Wars	1.000000	583
396	Empire Strikes Back, The	0.747981	367
1050	Return of the Jedi	0.672556	507
1025	Raiders of the Lost Ark	0.536117	420
91	Austin Powers: International Man of Mystery	0.377433	130

We can come with a similar analysis for any other movies from the list. Therefore, we can summarize the results in the next section.

Conclusions and Movie Recommendations

Based on the analysis, we can create the following function that recommends four similar movies to a movie that we just watched.

The following function recommends N movies similar to the Inp movie.

def Movie_Recommendations(Inp, N= 10, at_least= 100, Data = Movie_Matrix, Rate_df= Ratings):
    df_Movie = Data[Inp]
    Similar_Movies = Data.corrwith(df_Movie)
    Movie_corr = pd.DataFrame(Similar_Movies, columns=['Correlation'])
    Rate_df = Rate_df.set_index('Movie Title')
    Movie_corr = Movie_corr.join(Rate_df['Number of Ratings'])
    Move_Rec=Movie_corr[Movie_corr['Number of Ratings']> at_least].sort_values('Correlation',ascending=False).head(N)
    Move_Rec_list=list(Move_Rec.index)
    print(Back.CYAN +'Ten similar movies to: "%s":' % Inp)
    return Move_Rec_list[1:]

For example, consider Star Wars. For this movie, the function recommends the following movies

Movie_Recommendations('Star Wars')

Ten similar movies to: "Star Wars":

['Empire Strikes Back, The',
 'Return of the Jedi',
 'Raiders of the Lost Ark',
 'Austin Powers: International Man of Mystery',
 'Sting, The',
 'Indiana Jones and the Last Crusade',
 'Pinocchio',
 'Frighteners, The',
 'L.A. Confidential']

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References

1. Kaggle Dataset: MovieLens datasets

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